GD-Det: Low-Data Object Detection in Foggy Scenarios for Unmanned Aerial Vehicle Imagery Using Re-Parameterization and Cross-Scale Gather-and-Distribute Mechanisms Article Swipe
Rui Shi
,
Lili Zhang
,
Gaoxu Wang
,
Shutong Jia
,
Ning Zhang
,
Chensu Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17050783
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17050783
Unmanned Aerial Vehicles (UAVs) play an extremely important role in real-time object detection for maritime emergency rescue missions. However, marine accidents often occur in low-visibility weather conditions, resulting in poor image quality and a lack of object detection samples, which significantly reduces detection accuracy. To tackle these issues, we propose GD-Det, a low-data object detection model with high accuracy, specifically designed to handle limited sample sizes and low-quality images. The model is primarily composed of three components: (i) A lightweight re-parameterization feature extraction module which integrates RepVGG blocks into multi-concat blocks to enhance the model’s spatial perception and feature diversity during training. Meanwhile, it reduces computational cost in the inference phase through the re-parameterization mechanism. (ii) A cross-scale gather-and-distribute pyramid module, which helps to augment the relationship representation of four-scale features via flexible skip fusion and distribution strategies. (iii) A decoupled prediction module with three branches is to implement classification and regression, enhancing detection accuracy by combining the prediction values from tri-level features. (iv) We also use a domain-adaptive training strategy with knowledge transfer to handle low-data issues. We conducted low-data training and comparison experiments using our constructed dataset AFO-fog. Our model achieved an overall detection accuracy of 84.8%, which is superior to other models.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs17050783
- https://www.mdpi.com/2072-4292/17/5/783/pdf?version=1740382728
- OA Status
- gold
- Cited By
- 1
- References
- 47
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4407910019Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs17050783Digital Object Identifier
- Title
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GD-Det: Low-Data Object Detection in Foggy Scenarios for Unmanned Aerial Vehicle Imagery Using Re-Parameterization and Cross-Scale Gather-and-Distribute MechanismsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-24Full publication date if available
- Authors
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Rui Shi, Lili Zhang, Gaoxu Wang, Shutong Jia, Ning Zhang, Chensu WangList of authors in order
- Landing page
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https://doi.org/10.3390/rs17050783Publisher landing page
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https://www.mdpi.com/2072-4292/17/5/783/pdf?version=1740382728Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2072-4292/17/5/783/pdf?version=1740382728Direct OA link when available
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Remote sensing, Computer science, Scale (ratio), Computer vision, Artificial intelligence, Geology, Cartography, GeographyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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